Rapid Review Summit: an overview and initiation of a research agenda
Bibliographic record
Abstract
The demand for accelerated forms of evidence synthesis is on the rise, largely in response to requests by health care decision makers for expeditious assessment and up-to-date information about health care technologies and health services and programs. As a field, rapid review evidence synthesis is marked by a tension between the strategic priority to inform health care decision-making and the scientific imperative to produce robust, high-quality research that soundly supports health policy and practice. In early 2015, the Canadian Agency for Drugs and Technologies in Health convened a forum in partnership with the British Columbia Ministry of Health, the British Columbia Centre for Clinical Epidemiology and Evaluation, the Ottawa Hospital Research Institute, and the University of Pennsylvania. More than 150 evidence synthesis producers and end users attended the Rapid Review Summit: Then, Now and in the Future. The Summit program focused on the evolving role and practices of rapid reviews to support informed health care policy and clinical decision-making, including the uptake and use of health technology assessment. Our discussion paper highlights the important discussions that occurred during the Rapid Review Summit. It focuses on the initial development of a research agenda that resulted from the Summit presentations and discussions. The research topics centered on three key areas of interest: (1) how to conduct a rapid review; (2) investigating the validity and utility of rapid reviews; and (3) how to improve access to rapid reviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.498 | 0.494 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.045 | 0.044 |
| Open science | 0.012 | 0.048 |
| Research integrity | 0.048 | 0.059 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".